التفاصيل البيبلوغرافية
| العنوان: |
Gene Family Abundance Visualization based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis. |
| المؤلفون: |
Hai Thanh Nguyen, Tai Tan Phan, Tinh Cong Dao, Thao Minh N. Phan, Ta, Phuc Vinh D., Nguyen, Cham Ngoc T., Ngoc Huynh Pham, Hiep Xuan Huynh |
| المصدر: |
Journal of Engineering & Technological Sciences; 2021, Vol. 53 Issue 1, p99-115, 17p |
| مصطلحات موضوعية: |
FEATURE selection, DEEP learning, GENE families, METAGENOMICS, DIAGNOSIS, INFLAMMATORY bowel diseases |
| مستخلص: |
Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Engineering & Technological Sciences is the property of Institut Teknologi Bandung and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder